Useful R functions (preview)

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Sandbox vectors

Let’s define some vectors which can be used for demonstrations:

manyNumbers <- sample( 1:1000, 20 )
manyNumbers

 [1]  90 681 238 116 548 505 653  39 134 637 170 374 369 957 240 356 839  96  60 111

manyNumbersWithNA <- sample( c( NA, NA, NA, manyNumbers ) )
manyNumbersWithNA

 [1]  NA  NA 134 505  NA  39 240  96  90 111 957 839 116 170 356  60 238 681 369 653 374 548 637

duplicatedNumbers <- sample( 1:5, 10, replace = TRUE )
duplicatedNumbers

 [1] 3 5 1 5 3 5 1 5 1 5

letters

 [1] "a" "b" "c" "d" "e" "f" "g" "h" "i" "j" "k" "l" "m" "n" "o" "p" "q" "r" "s" "t" "u" "v" "w" "x" "y" "z"

LETTERS

 [1] "A" "B" "C" "D" "E" "F" "G" "H" "I" "J" "K" "L" "M" "N" "O" "P" "Q" "R" "S" "T" "U" "V" "W" "X" "Y" "Z"

mixedLetters <- c( sample( letters, 5 ), sample( LETTERS, 5 ) )
mixedLetters

 [1] "j" "v" "l" "i" "m" "Z" "S" "A" "N" "C"

Are all/any elements TRUE

Input: logical vector
Output: single logical value
Task: try, understand what happens when you use manyNumbersWithNA instead of manyNumbers.

all( manyNumbers <= 1000 )

[1] TRUE

all( manyNumbers <= 500 )

[1] FALSE

any( manyNumbers > 1000 )

[1] FALSE

any( manyNumbers > 500 )

[1] TRUE

all( !is.na( manyNumbers ) )

[1] TRUE

any( is.na( manyNumbers ) )

[1] FALSE

Which elements are TRUE

Input: logical vector Output: vector of numbers (positions)

which( manyNumbers > 900 )

[1] 14

which( manyNumbersWithNA > 900 )

[1] 11

which( is.na( manyNumbersWithNA ) )

[1] 1 2 5

Filtering vector elements

Input: any vector and filtering condition
Output: elements of the input vector
Note: several ways to get the same effect

manyNumbers[ manyNumbers > 900 ] # indexing by logical vector

[1] 957

manyNumbers[ which( manyNumbers > 900 ) ] # indexing by positions

[1] 957

somePositions <- which( manyNumbers > 900 )
manyNumbers[ somePositions ]

[1] 957

Are some elements among other elements

Input: two vectors
Output: a logical vector corresponding to the first input vector

"A" %in% LETTERS

[1] TRUE

c( "X", "Y", "Z" ) %in% LETTERS

[1] TRUE TRUE TRUE

all( c( "X", "Y", "Z" ) %in% LETTERS )

[1] TRUE

all( mixedLetters %in% LETTERS )

[1] FALSE

any( mixedLetters %in% LETTERS )

[1] TRUE

mixedLetters[ mixedLetters %in% LETTERS ]

[1] "Z" "S" "A" "N" "C"

mixedLetters[ !( mixedLetters %in% LETTERS ) ]

[1] "j" "v" "l" "i" "m"

manyNumbers %in% 300:600

 [1] FALSE FALSE FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE FALSE FALSE  TRUE FALSE FALSE
[19] FALSE FALSE

which( manyNumbers %in% 300:600 )

[1]  5  6 12 13 16

sum( manyNumbers %in% 300:600 )

[1] 5

Pick one of two (three) depending on condition

Input: a logical vector and two vectors additional vectors (for TRUE, for FALSE)
Output: elements of the additional vectors
Note: it can take care of NAs

if_else( manyNumbersWithNA >= 500, "large", "small" )

 [1] NA      NA      "small" "large" NA      "small" "small" "small" "small" "small" "large" "large" "small" "small"
[15] "small" "small" "small" "large" "small" "large" "small" "large" "large"

if_else( manyNumbersWithNA >= 500, "large", "small", "UNKNOWN" )

 [1] "UNKNOWN" "UNKNOWN" "small"   "large"   "UNKNOWN" "small"   "small"   "small"   "small"   "small"   "large"  
[12] "large"   "small"   "small"   "small"   "small"   "small"   "large"   "small"   "large"   "small"   "large"  
[23] "large"

# here integer 0L is needed instead of real 0.0 
# manyNumbersWithNA contains integer numbers and the method complains
if_else( manyNumbersWithNA >= 500, manyNumbersWithNA, 0L )

 [1]  NA  NA   0 505  NA   0   0   0   0   0 957 839   0   0   0   0   0 681   0 653   0 548 637

Duplicates and unique elements

Input: a vector

unique( duplicatedNumbers )

[1] 3 5 1

unique( c( NA, duplicatedNumbers, NA ) )

[1] NA  3  5  1

duplicated( duplicatedNumbers )

 [1] FALSE FALSE FALSE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE

Positions of max/min elements

which.max( manyNumbersWithNA )

[1] 11

manyNumbersWithNA[ which.max( manyNumbersWithNA ) ]

[1] 957

which.min( manyNumbersWithNA )

[1] 6

manyNumbersWithNA[ which.min( manyNumbersWithNA ) ]

[1] 39

range( manyNumbersWithNA, na.rm = TRUE )

[1]  39 957

Sorting/ordering of vectors

manyNumbersWithNA

 [1]  NA  NA 134 505  NA  39 240  96  90 111 957 839 116 170 356  60 238 681 369 653 374 548 637

sort( manyNumbersWithNA )

 [1]  39  60  90  96 111 116 134 170 238 240 356 369 374 505 548 637 653 681 839 957

sort( manyNumbersWithNA, na.last = TRUE )

 [1]  39  60  90  96 111 116 134 170 238 240 356 369 374 505 548 637 653 681 839 957  NA  NA  NA

sort( manyNumbersWithNA, na.last = TRUE, decreasing = TRUE )

 [1] 957 839 681 653 637 548 505 374 369 356 240 238 170 134 116 111  96  90  60  39  NA  NA  NA

manyNumbersWithNA[1:5]

[1]  NA  NA 134 505  NA

order( manyNumbersWithNA[1:5] )

[1] 3 4 1 2 5

rank( manyNumbersWithNA[1:5] )

[1] 3 4 1 2 5

sort( mixedLetters )

 [1] "A" "C" "i" "j" "l" "m" "N" "S" "v" "Z"

Ranking of vectors

manyDuplicates <- sample( 10:15, 10, replace = TRUE )
rank( manyDuplicates )

 [1]  2.0  5.5  8.5  2.0  7.0  8.5  5.5 10.0  4.0  2.0

rank( manyDuplicates, ties.method = "min" )

 [1]  1  5  8  1  7  8  5 10  4  1

rank( manyDuplicates, ties.method = "random" )

 [1]  3  5  8  2  7  9  6 10  4  1

Rounding numbers

v <- c( -1, -0.5, 0, 0.5, 1, rnorm( 10 ) )
v

 [1] -1.000000000 -0.500000000  0.000000000  0.500000000  1.000000000  1.457656334 -1.505883670  1.085477051
 [9] -0.738247661  1.631141894  0.495322502 -0.033199100 -0.870309233 -0.007816511  1.413983911

round( v, 0 )

 [1] -1  0  0  0  1  1 -2  1 -1  2  0  0 -1  0  1

round( v, 1 )

 [1] -1.0 -0.5  0.0  0.5  1.0  1.5 -1.5  1.1 -0.7  1.6  0.5  0.0 -0.9  0.0  1.4

round( v, 2 )

 [1] -1.00 -0.50  0.00  0.50  1.00  1.46 -1.51  1.09 -0.74  1.63  0.50 -0.03 -0.87 -0.01  1.41

floor( v )

 [1] -1 -1  0  0  1  1 -2  1 -1  1  0 -1 -1 -1  1

ceiling( v )

 [1] -1  0  0  1  1  2 -1  2  0  2  1  0  0  0  2

Naming vector elements

heights <- c( Amy = 166, Eve = 170, Bob = 177 )
heights

Amy Eve Bob 
166 170 177

names( heights )

[1] "Amy" "Eve" "Bob"

names( heights ) <- c( "AMY", "EVE", "BOB" )
heights

AMY EVE BOB 
166 170 177

heights[[ "EVE" ]]

[1] 170

Generating grids

expand_grid( x = c( 1:3, NA ), y = c( "a", "b" ) )

# A tibble: 8 × 2
      x y    
  <int> <chr>
1     1 a    
2     1 b    
3     2 a    
4     2 b    
5     3 a    
6     3 b    
7    NA a    
8    NA b

Generating combinations

combn( c( "a", "b", "c", "d", "e" ), m = 2, simplify = TRUE )

     [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10]
[1,] "a"  "a"  "a"  "a"  "b"  "b"  "b"  "c"  "c"  "d"  
[2,] "b"  "c"  "d"  "e"  "c"  "d"  "e"  "d"  "e"  "e"

combn( c( "a", "b", "c", "d", "e" ), m = 3, simplify = TRUE )

     [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10]
[1,] "a"  "a"  "a"  "a"  "a"  "a"  "b"  "b"  "b"  "c"  
[2,] "b"  "b"  "b"  "c"  "c"  "d"  "c"  "c"  "d"  "d"  
[3,] "c"  "d"  "e"  "d"  "e"  "e"  "d"  "e"  "e"  "e"

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